Kubernetes Autoscaling Myths: Why HPA Alone Won’t Fix Your Resource Problems

This is the multi-part blog series in the first part I covered up an operator’s view into the Kubernetes resource paradox. Learn why most clusters waste 40–60% of their capacity, how resource requests really work, and why overprovisioning is a rational response to fear — not incompetence . And in the second part I explained why Kubernetes resource overprovisioning happens, how it quietly inflates cloud costs, and what real-world strategies DevOps teams use to regain control over CPU, memory, and GPU usage . Horizontal Pod Autoscaler is often treated as Kubernetes’ automatic scaling solution, but in reality it only works when requests, metrics, and workload behavior are understood. This deep dive explains why autoscaling frequently fails in production and how to design scaling strategies that actually work at scale. By the time most teams adopt autoscaling in Kubernetes, they’ve already run into the limitations of static resource allocation. Traffic fluctuates, workloads behave unpredic